Methods for RNA splicing variations detection, quantification, visualization, and association from large heterogeneous datasets

来自大型异构数据集的 RNA 剪接变异检测、量化、可视化和关联的方法

基本信息

批准号：
9500401
负责人：
Yoseph Barash
金额：
$ 43.25万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-05-01 至 2022-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9500401
关键词：
Adoption Affect Algorithms Alleles Alternative Splicing Area Big Data Biochemical Biological Biological Assay Biological Markers Cloud Computing Code Complex Computer software Computing Methodologies Coupled DNA Sequence Alteration Data Data Set Defect Detection Disease Evaluation Event Exons Finding by Cause Galaxy Gene Expression Genes Genetic Genetic Research Genetic Variation Genome Genotype Genotype-Tissue Expression Project Goals Heritability High-Throughput Nucleotide Sequencing Human Human Genetics Imagery Individual Knowledge Link Maps Measures Medical Methodology Methods Modeling Mutation Patients Pattern Performance Persons Privacy Protein Isoforms Quantitative Trait Loci RNA RNA Processing RNA Splicing Research Research Personnel Reverse Transcriptase Polymerase Chain Reaction Sampling Signal Transduction Source Statistical Models Testing Tissues Transcript Validation Variant Work base cell type cloud based cohort disorder risk experience genetic variant genome wide association study human tissue improved laptop open source programs scale up success support tools tool tool development trait transcriptome transcriptome sequencing user-friendly web services

项目摘要

Abstract The goal of this research program is to develop methods and tools to analyze large heterogeneous RNA-seq data sets to better understand RNA splicing. The vast majority of human genes are alternatively spliced and variation in splicing has been shown to be associated with complex disease risk. Despite the wide spread adoption of affordable high throughput sequencing, variation in RNA splicing has remained understudied due to the limitations of short read sequencing data and the computational challenges associated with accurate transcript-level quantification of gene expression. We propose to develop methods to improve the detection, quantification, and visualization of complex splicing events. We will further develop methods to identify genetic variants associated with complex splicing variation and to characterize the mechanisms by which splicing variation affects complex traits. Importantly, the variations and mechanisms predicted by our methods will be replicated in independent cohorts and experimentally validated using orthogonal methods. The computational methods and software we will develop will be applied both to publicly available data and data generated by our groups. We propose to leverage not only our expertise but also our existing code base and tools. The tools will support both standalone and cloud based execution for scaling up analysis, and will integrate with existing tools for downstream analysis.

抽象的该研究项目的目标是开发分析大型异质 RNA-seq 的方法和工具数据集以更好地了解 RNA 剪接。绝大多数人类基因都是选择性剪接的剪接变异已被证明与复杂疾病风险相关。尽管流传广泛采用负担得起的高通量测序，RNA 剪接的变化仍然没有得到充分研究，因为短读长测序数据的局限性以及与准确相关的计算挑战基因表达的转录水平定量。我们建议开发方法来改进检测，复杂剪接事件的量化和可视化。我们将进一步开发鉴定基因的方法与复杂剪接变异相关的变体并表征剪接机制变异影响复杂的性状。重要的是，我们的方法预测的变化和机制将是在独立队列中复制并使用正交方法进行实验验证。计算性的我们将开发的方法和软件将应用于公开数据和我们生成的数据组。我们建议不仅利用我们的专业知识，而且利用我们现有的代码库和工具。这些工具将支持独立和基于云的执行以扩大分析范围，并将与现有的集成下游分析工具。